ObjectDetectionRecognitionTracking (without deep learning)

under construction.

Visual Object Detection, Recognition and Tracking (without Deep Learning)

—State of Art Object Detection and Classification

—Global/local features (Harris, FAST, SIFT, SURF, HOG, LBP, BRIEF, BRISK, FREAK, ORB)

—Kd-tree, LSH, min-hash, inverted file;

—part-based (constellation model, pictorial structure, implicit shape model, deformable model)

—Pose estimation

—bag of words (codebook, pyramid match kernel, spatial pyramid match, vocabulary tree, pLSA, LDA)

—VLAD, Fisher kernel, Hamming embedding, product quant.

—Machine learning: generative/discriminative model

—Efficiency in Detection/Classification

—Divide-and-conquer, branch-and-bound, coarse-to-fine, DP, selective search by segmentation.

—Open set problem

—Data unbalancing problem

—Face detection/recognition

—Text detection/recognition

—Scene parsing/semantic segmentation

—Data set and evaluation metric

—Object Tracking

—State-of-art methods of Object Tracking

—Representation Scheme in Tracking

—Search Mechanism in Tracking

—Model Update in Tracking

—Context in Tracker, Fusion of Trackers

—Multiple object tracking

—3-D Model-based tracking

—SLAM (feature/pixel tracking)

—Appendix A: Background modeling and subtraction

—Appendix B: Action/Event Detection/Classification